

import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Tuple       					=			LayoutLMTokenizer
						_A    :				Optional[int]       					=			LayoutLMTokenizerFast
						_A    :				int       					=			True
						_A    :				Optional[int]       					=			True





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									super().setUp()

									a_						:						Optional[Any] 							= [
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """want""",
									    """##want""",
									    """##ed""",
									    """wa""",
									    """un""",
									    """runn""",
									    """##ing""",
									    """,""",
									    """low""",
									    """lowest""",
									]
									a_						:						Dict 							= os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES["""vocab_file"""]    )
									with open(self.vocab_file  ,      """w"""  ,      encoding="""utf-8"""    ) as vocab_writer:
												vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )





						def 			UpperCamelCase__					(    self  ,      **_lowercase    )     -> Any:
									return LayoutLMTokenizer.from_pretrained(self.tmpdirname  ,      **_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Any:
									a_						:						List[Any] 							= """UNwant\u00E9d,running"""
									a_						:						Any 							= """unwanted, running"""
									return input_text, output_text





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Dict 							= self.tokenizer_class(self.vocab_file    )

									a_						:						Tuple 							= tokenizer.tokenize("""UNwant\u00E9d,running"""    )
									self.assertListEqual(_lowercase  ,      ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]    )
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase    )  ,      [7, 4, 5, 10, 8, 9]    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									pass





from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch



@require_torch
@require_tf
class        A__(a_							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						List[Any] 							= SMALL_MODEL_IDENTIFIER
									a_						:						Optional[int] 							= """pt"""
									a_						:						Union[str, Any] 							= """tf"""





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> str:
									a_						:						Union[str, Any] 							= AutoModel.from_pretrained(self.test_model    )
									model_pt.save_pretrained(_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> List[str]:
									a_						:						int 							= TFAutoModel.from_pretrained(self.test_model  ,      from_pt=_lowercase    )
									model_tf.save_pretrained(_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						str 							= """mock_framework"""

									# Framework provided - return whatever the user provides
									a_						:						Optional[int] 							= FeaturesManager.determine_framework(self.test_model  ,      _lowercase    )
									self.assertEqual(_lowercase  ,      _lowercase    )

									# Local checkpoint and framework provided - return provided framework
									# PyTorch checkpoint
									with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(_lowercase    )
												a_						:						Optional[int] 							= FeaturesManager.determine_framework(_lowercase  ,      _lowercase    )
												self.assertEqual(_lowercase  ,      _lowercase    )

									# TensorFlow checkpoint
									with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(_lowercase    )
												a_						:						List[str] 							= FeaturesManager.determine_framework(_lowercase  ,      _lowercase    )
												self.assertEqual(_lowercase  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									# PyTorch checkpoint
									with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(_lowercase    )
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# TensorFlow checkpoint
									with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(_lowercase    )
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )
												self.assertEqual(_lowercase  ,      self.framework_tf    )

									# Invalid local checkpoint
									with TemporaryDirectory() as local_invalid_ckpt:
												with self.assertRaises(_lowercase    ):
															a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						int 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ):
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# PyTorch not in environment -> use TensorFlow
									a_						:						Dict 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_tf    )

									# Both in environment -> use PyTorch
									a_						:						Optional[Any] 							= MagicMock(return_value=_lowercase    )
									a_						:						List[Any] 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ), patch(
									    """transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												a_						:						Any 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# Both not in environment -> raise error
									a_						:						List[str] 							= MagicMock(return_value=_lowercase    )
									a_						:						Optional[Any] 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ), patch(
									    """transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												with self.assertRaises(_lowercase    ):
															a_						:						Dict 							= FeaturesManager.determine_framework(self.test_model    )


